Find research datasets worth reusing

opencc-by-4.0Oct 2022View details →

An Empirical Study of Textual and Structural Statistical Models for Maven-Based Build Systems

Replication package for the manuscript entitled "An Empirical Study of Textual and Structural Statistical Models for Maven-Based Build Systems". See README.md file for details about how to use the package.

openMay 2023View details →

Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings (Dataset v1.2.1)

THE PUBLICATION The data set provided is complementary to the thermal comfort review by Mamulova et al., 2023, titled "Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings": <a href="https://doi.org/10.1016/j.buildenv.2023.110502">Link to full publication</a>. The scoping review examines 77 multi-domain thermal comfort studies and initiates a discussion on model scalability; a model parameter which facilitates the understanding and prediction of thermal comfort conditions in real-world practice. THE DATA This database contains 27 scalability parameters per study which are used to analyse current research practices. For the results, please consult the review publication, as this database only contains raw data. For clarity, a legend of the scalability parameters is provided below. *** PLEASE NOTE *** This data set may be utilised, altered and/or expanded. However, you are kindly asked to cite this data set, the review publication (if applicable) and contact the corresponding author at eugenemamulova@gmail.com.  <table> <tbody> <tr> <td>Citation</td> <td>Citation number used in Mamulova et al.,"Multi-Domain Thermal Comfort Models for Office Buildings: Are Current Practices Scalable?", (2023)</td> <td>E.g. 1</td> </tr> <tr> <td>First Author</td> <td>Surname of the main author, for reference purposes only.</td> <td>E.g. Al-Atrash</td> </tr> <tr> <td>Publication</td> <td>Publication year</td> <td>E.g. 2020</td> </tr> <tr> <td>Dependent A</td> <td>List of variables used to measure thermal perception</td> <td>E.g. Neutral  temperature/ Thermal sensation</td> </tr> <tr> <td>Dependent B</td> <td>Scale used to measure each dependent variable</td> <td> </td> </tr> <tr> <td>Interaction A</td> <td>List of interaction effect(s) included in the explanatory/predictive model(s) </td> <td>E.g. Thermal and age/ Thermal and acoustical and personality</td> </tr> <tr> <td>Interaction B</td> <td>Is/are the effect(s) statistically significant?</td> <td>E.g. yes/ no/ (unknown)</td> </tr> <tr> <td>Crossed A</td> <td>List of crossed effect(s) included in the explanatory/predictive model(s) </td> <td>E.g. Acoustical/ Personality/ Age</td> </tr> <tr> <td>Crossed B</td> <td> </td> <td>*Note: Temperature is a main effect and is not included in the list</td> </tr> <tr> <td>Explanatory A</td> <td>Type of explanatory model</td> <td>E.g. Observation/ Statistical/ N/A</td> </tr> <tr> <td>Explanatory B</td> <td>Description of the explanatory model</td> <td>E.g. Asymptotic General Symmetry Test to check significance of difference in thermal perception between window conditions</td> </tr> <tr> <td>Predictive A</td> <td>Does the article include a predictive model?</td> <td>E.g. yes/ no</td> </tr> <tr> <td>Predictive B</td> <td>Type of predictive algorithm</td> <td>E.g. Logistic regression/ N/A</td> </tr> <tr> <td>Predictive C</td> <td>Description or formulation of the predictive model</td> <td>E.g. Probability of feeling too hot and probability of feeling too cold in relation to sound pressure level</td> </tr> <tr> <td>Performance</td> <td>Reported predictive performance</td> <td>E.g. Accuracy = 80%/ F-score = 0.8/ N/A</td> </tr> <tr> <td>Location</td> <td>City in which the measurements take place</td> <td>E.g. Paris</td> </tr> <tr> <td>Period</td> <td>Period over which the measurements take place</td> <td>E.g. Jan-Feb 2020</td> </tr> <tr> <td>Start time</td> <td>Time of day at which the measurements begin</td> <td>*Note: Time of day is not reported for most field studies. For this reason, time of day is only recorded for laboratory experiements.</td> </tr> <tr> <td>Study type</td> <td>Type of building and whether the experimental conditions are controlled by the experiment leader</td> <td>E.g. Field (controlled)/ Field (uncontrolled)/ Lab (controlled)/ Lab (uncontrolled)</td> </tr> <tr> <td>Building layout</td> <td>Building layout</td> <td>E.g. Laboratory office (LO)/ Laboratory neutral (LN)/ Field office (FO)</td> </tr> <tr> <td>Exposure</td> <td>Exposure of the participant, in minutes, to the experimental conditions, excluding preparation time</td> <td>*Note: Exposure is not reported for most field studies. For this reason, exposure is only recorded for laboratory experiements and is assumed to be longer than 60 minutes.</td> </tr> <tr> <td>Number of buildings/chambers</td> <td>Number of different locations used for conducting measurements</td> <td>E.g. 1</td> </tr> <tr> <td>Number of participants</td> <td>Number of individuals who take part in each experiment</td> <td>*Note: Outliers who are subsequently excluded from the modelling phase are not  included.</td> </tr> <tr> <td>Survey type</td> <td>Description of the type of survey used for subjective measurements</td> <td>E.g. Longitudinal questionnaire/ Transverse questionnaire/ N/A</td> </tr> <tr> <td>Survey content</td> <td>Are the contents of the survey provided in the article?</td> <td>E.g. Available/ unavailable</td> </tr> <tr> <td>Survey source</td> <td>Is/are the source(s) of the survey items mentioned in the article?</td> <td>E.g. Available/ unavailable</td> </tr> <tr> <td>Survey reliability</td> <td>Is the reliability of the survey items reported in the article?</td> <td>E.g. Available/ unavailable</td> </tr> <tr> <td>Survey duration</td> <td>Is the survey duration reported in the article?</td> <td>E.g. Available/ unavailable</td> </tr> <tr> <td>Context A</td> <td>Overview of the contextual information provided by the authors</td> <td>E.g. Room layout/ Room dimennsions</td> </tr> <tr> <td>Context B</td> <td>Qualitative/quantitative contextual information</td> <td>E.g. Figure containing room layout/ 3m x 3m x 5m</td> </tr> <tr> <td>Contextual variables A</td> <td>List of contextual variable(s) measured by the researchers (see Fig. A.)</td> <td>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</td> </tr> <tr> <td>Contextual variables B</td> <td>Range of values included in the experiment and their respective units.</td> <td>E.g. figure</td> </tr> <tr> <td>Social variables A</td> <td>List of social variable(s) measured by the researchers (see Fig. A.)</td> <td>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</td> </tr> <tr> <td>Social variables B</td> <td>Range of values included in the experiment and their respective units.</td> <td>E.g. [1,2,3,4,5]</td> </tr> <tr> <td>Personal variables A</td> <td>List of contextual variable(s) measured by the researchers (see Fig. A.)</td> <td>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</td> </tr> <tr> <td>Personal variables B</td> <td>Range of values included in the experiment and their respective units.</td> <td>E.g. [red, blue]</td> </tr> <tr> <td>Physical variables A</td> <td>List of physical variable(s) measured by the researchers (see Fig. A.)</td> <td>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</td> </tr> <tr> <td>Physical variables B</td> <td>Range of values included in the experiment and their respective units</td> <td>E.g. dB(A)</td> </tr> <tr> <td>Full-factorial</td> <td>Is/are the experiment(s) full-factorial?</td> <td>*Note: Uncontrolled field experiments are automatically labelled as fractional factorial.</td> </tr> <tr> <td>(Participant) Control</td> <td>Do participants have control over one or more experimental conditions?</td> <td>E.g. Yes/ No</td> </tr> <tr> <td>With/between subjects</td> <td>Are the experimental conditions shared between or within the participants?</td> <td>E.g. w/ b</td> </tr> <tr> <td>Fixed variables A</td> <td>List of variables reported as constant during the measurements</td> <td>E.g. Relative humidity/ Metabolic rate</td> </tr> <tr> <td>Fixed variables A</td> <td>(Range of) values and their respective units.</td> <td>E.g. 30-40%/ 1.2 met</td> </tr> <tr> <td>Summary</td> <td>Description of the research outome (outcome of the explanatory and/or predictive modelling)</td> <td>E.g. Lack of perceived control has a significant negative effect on neutral temperatures.</td> </tr> <tr> <td>Evaluation</td> <td>Are the participants invited to evaluate their experience once the experiment has been completed? </td> <td>E.g. Yes/ no</td> </tr> </tbody> </table> Note: The data in v1.1.0 has not yet been optimised for analytics.

opencc-zeroJun 2023View details →

Simulated population time series used to build and test a model of accuracy for population-based global biodiversity indicators

Global biodiversity is facing a crisis, which must be solved through effective policies and on-the-ground conservation. But governments, NGOs, and scientists need reliable indicators to guide research, conservation actions, and policy decisions. Developing reliable indicators is challenging because the data underlying those tools is incomplete and biased. For example, the Living Planet Index tracks the changing status of global vertebrate biodiversity, but taxonomic, geographic and temporal gaps and biases are present in the aggregated data used to calculate trends. But without a basis for real-world comparison, there is no way to directly assess an indicator's accuracy or reliability. Instead, a modelling approach can be used. We developed a model of trend reliability, using simulated datasets as stand-ins for the "real world", degraded samples as stand-ins for indicator datasets (e.g. the Living Planet Database), and a distance measure to quantify reliability by comparing sampled to unsampled trends. The model revealed that the proportion of species represented in the database is not always indicative of trend reliability. Important factors are the number and length of time series, as well as their mean growth rates and variance in their growth rates, both within and between time series. We found that many trends in the Living Planet Index need more data to be considered reliable, particularly trends across the global south. In general, bird trends are the most reliable, while reptile and amphibian trends are most in need of additional data. We simulated three different solutions for reducing data deficiency, and found that collating existing data (where available) is the most efficient way to improve trend reliability, and that revisiting previously-studied populations is a quick and efficient way to improve trend reliability until new long-term studies can be completed and made available.

opencc-by-4.0Jun 2023View details →

Urban Building Energy Modelling for the Renovation Wave: A Bespoke Approach Based on EPC Databases

Dataset associated to the article: Rodríguez-Álvarez, J.Urban Building Energy Modelling for the Renovation Wave: A Bespoke Approach Based on EPC Databases. Buildings 2023, 13, x. It contains filtered EPC datasets as xls and csv and shapefiles with the buildings' geometry and estimated energy loads

opencc-by-4.0Jul 2023View details →

Simulation data for the office cell building energy model with the attached overhang

Simulation data for 729,000 variants of the office cell building model with the overhang attached over the window. The variants are determined by the overhang depth and height, location, presence of obstacles, orientation and cooling and heating set points. The office cell model is described in the manuscript "Predicting the shape of loads for an office cell with an overhang from a small number of building energy simulations".

opencc-bySep 2021View details →

building and LODs Model

3D models and LOD models blender and substance painter you can use for games and free download credit from kyyy_24 follow my Artstation Account https://kyyy_ndr.artstation.com/ follow my instagram artwork https://www.instagram.com/laart851/ Source: Objaverse 1.0 / Sketchfab

publicNov 2025View details →

Data for: How do we measure and increase systems thinking? Comparing self-reported and performative metrics in response to building causal loop models

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publicSep 2020View details →

How to build a dinosaur: musculoskeletal modelling and simulation of locomotor biomechanics in extinct animals

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publicJul 2024View details →

Data from: Genetic variants and clinical indicators used to build nomogram model

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publicJun 2023View details →

Simulated population time series used to build and test a model of accuracy for population-based global biodiversity indicators

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opencc-by-4.0Oct 2020View details →

Building DEVS Models from the Functional Design of Software Architecture Components to Estimate Quality

Presentation of the paper titled "Building DEVS Models from the Functional Design of Software Architecture Components to Estimate Quality".

opencc-byNov 2019View details →

Century Building model

A model make in blender, (texture used in textures.com) Source: Objaverse 1.0 / Sketchfab

openepl-2.0Dec 2023View details →

OPEN-KTH-3dMODELS: An Open Dataset of Building Models at KTH Campus Valhallavägen

OPEN-KTH-3dMODELS: An open dataset of building models at KTH Campus Valhallavägen <ul> <li>Open-KTH-3dModels is a subproject of the AD-EYE testbed for Automated Driving and Intelligent Transportation Systems.</li> <li>The dataset comprises of a series .blend files that have prominent buildings from KTH campus Valhallavägen. </li> <li>The dataset also contains PreScan compatible models that can be used wtih AD-EYE (<a href="https://www.adeye.se/open-kth-3dmodels">https://www.adeye.se/</a>)</li> </ul> Visualisation video: https://www.youtube.com/watch?v=F6NfCiul3oE Learn more at <a href="https://www.adeye.se/open-kth-3dmodels">https://www.adeye.se/open-kth-3dmodels</a> or contact <a href="mailto:adeye@md.kth.se">adeye@md.kth.se</a>   The AD-EYE testbed is based on the design presented in the work "AD-EYE: A Co-Simulation Platform for Early Verification of Functional Safety Concepts"   Original paper: <a href="https://doi.org/10.4271/2019-01-0126">https://doi.org/10.4271/2019-01-0126</a> Preprint available at: <a href="https://arxiv.org/abs/1912.00448">https://arxiv.org/abs/1912.00448</a> Citation: Naveen Mohan, Martin Törngren, "AD-EYE: A Co-Simulation Platform for Early Verification of Functional Safety Concepts", SAE Technical Paper 19AE-0203/2019-01-0126, <a href="https://doi.org/10.4271/2019-01-0126">https://doi.org/10.4271/2019-01-0126</a>   Notes: Modelling work primarily performed by Lester Jose, during his internship with AD-EYE.

opencc-by-4.0Feb 2024View details →

Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP2-RCP4.5)

As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment. This dataset contains the cross-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 2 and the representative concentration pathway (RCP) used was RCP 4.5. More information about SSP and RCP can be referred to O'Neill et al. (2020). Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location. More information about the six selected CMIP6 GCMs: ACCESS-CM2 - http://dx.doi.org/10.1071/ES19040 BCC-CSM2-MR - https://doi.org/10.5194/gmd-14-2977-2021 CNRM-ESM2-1- https://doi.org/10.1029/2019MS001791 MPI-ESM1-2-HR - https://doi.org/10.5194/gmd-12-3241-2019 MRI-ESM2-0 - https://doi.org/10.2151/jmsj.2019-051 NorESM2-MM - https://doi.org/10.5194/gmd-13-6165-2020 Additional references: O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework. Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0 Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228. Please cite the following if this data is used in any research or project: Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: <a href="http://dx.doi.org/10.1145/3600100.3626637" target="_blank" rel="noreferrer noopener">10.1145/3600100.3626637</a>   Cross-Model Version: <div> <div> Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719204, Feb 2024. [<a href="10719204" target="_blank" rel="noopener">Data</a>]  Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719178, Feb 2024. [<a href="../records/10719178" target="_blank" rel="noopener">Data</a>]  Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10698921, Feb 2024. [<a href="../records/10698921" target="_blank" rel="noopener">Data</a>]  Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (Cross-Model version-SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10420668, Dec 2023. [<a href="../records/10420668" target="_blank" rel="noopener">Data</a>]    Model-specific Version: Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729277, Feb 2024. [<a href="../records/10729277" target="_blank" rel="noopener">Data</a>]  Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729279, Feb 2024. [<a href="../records/10729279" target="_blank" rel="noopener">Data</a>] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729223, Feb 2024. [<a href="../records/10729223" target="_blank" rel="noopener">Data</a>]  Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729201, Feb 2024. [<a href="../records/10729201" target="_blank" rel="noopener">Data</a>] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729157, Feb 2024. [<a href="../records/10729157" target="_blank" rel="noopener">Data</a>]    Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729199, Feb 2024. [<a href="../records/10729199" target="_blank" rel="noopener">Data</a>] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (East and South – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8335814, Sept 2023. [<a href="../records/8335814" target="_blank" rel="noopener">Data</a>] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8338548, Sept 2023. [<a href="../records/8338548" target="_blank" rel="noopener">Data</a>]  </div> </div>   Representative Cities Version: Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F6939750%23.YwYzp3bMKUk&data=05%7C01%7Clif2%40ornl.gov%7C26cbed91b56e40d4014708dbc0976975%7Cdb3dbd434c4b45449f8a0553f9f5f25e%7C1%7C0%7C638315528798318118%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=S8Z0mjWDMqelFJkp2mfNBVqaiDCdM3AXjQ7PDPEBIu4%3D&reserved=0">Data</a>]

opencc-by-4.0Feb 2024View details →

Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP5-RCP8.5)

As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment. This dataset contains the cross-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 5 and the representative concentration pathway (RCP) used was RCP 8.5. More information about SSP and RCP can be referred to O'Neill et al. (2020). Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location. More information about the six selected CMIP6 GCMs: ACCESS-CM2 - http://dx.doi.org/10.1071/ES19040 BCC-CSM2-MR - https://doi.org/10.5194/gmd-14-2977-2021 CNRM-ESM2-1- https://doi.org/10.1029/2019MS001791 MPI-ESM1-2-HR - https://doi.org/10.5194/gmd-12-3241-2019 MRI-ESM2-0 - https://doi.org/10.2151/jmsj.2019-051 NorESM2-MM - https://doi.org/10.5194/gmd-13-6165-2020 Additional references: O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework. Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0 Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228. Please cite the following if this data is used in any research or project: Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: <a href="http://dx.doi.org/10.1145/3600100.3626637" target="_blank" rel="noreferrer noopener">10.1145/3600100.3626637</a>   Cross-Model Version: <div> <div> Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719204, Feb 2024. [<a href="10719204" target="_blank" rel="noopener">Data</a>]  Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719178, Feb 2024. [<a href="../records/10719178" target="_blank" rel="noopener">Data</a>]  Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10698921, Feb 2024. [<a href="../records/10698921" target="_blank" rel="noopener">Data</a>]  Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (Cross-Model version-SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10420668, Dec 2023. [<a href="../records/10420668" target="_blank" rel="noopener">Data</a>]    Model-specific Version: Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729277, Feb 2024. [<a href="../records/10729277" target="_blank" rel="noopener">Data</a>]  Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729279, Feb 2024. [<a href="../records/10729279" target="_blank" rel="noopener">Data</a>] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729223, Feb 2024. [<a href="../records/10729223" target="_blank" rel="noopener">Data</a>]  Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729201, Feb 2024. [<a href="../records/10729201" target="_blank" rel="noopener">Data</a>] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729157, Feb 2024. [<a href="../records/10729157" target="_blank" rel="noopener">Data</a>]    Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729199, Feb 2024. [<a href="../records/10729199" target="_blank" rel="noopener">Data</a>] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (East and South – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8335814, Sept 2023. [<a href="../records/8335814" target="_blank" rel="noopener">Data</a>] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8338548, Sept 2023. [<a href="../records/8338548" target="_blank" rel="noopener">Data</a>]  </div> </div>   Representative Cities Version: Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F6939750%23.YwYzp3bMKUk&data=05%7C01%7Clif2%40ornl.gov%7C26cbed91b56e40d4014708dbc0976975%7Cdb3dbd434c4b45449f8a0553f9f5f25e%7C1%7C0%7C638315528798318118%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=S8Z0mjWDMqelFJkp2mfNBVqaiDCdM3AXjQ7PDPEBIu4%3D&reserved=0">Data</a>]

opencc-by-4.0Apr 2024View details →

Examining factors affecting sustainable performance of building projects using structural equation modeling

Open the record for dataset details and reuse information.

opencc-by-4.0Apr 2024View details →

A Hierarchical Model of Accelerating Factors to Promote Urban Renewal and Reconstruction of Unsafe and Old Buildings

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opencc-by-4.0Nov 2024View details →

Artifact for the paper "Towards Model-Driven Heat Pump Control in a Multi-Story Building"

This is a reproducibility package for the paper "Towards Model-Driven Heat Pump Control in a Multi-Story Building". Domestic heating systems can provide significant energy flexibility when integrated with heat pumps and hot water buffer tanks, especially with fluctuating day-ahead energy prices. However, optimizing these systems in large buildings with shared resources poses crucial challenges. While most existing studies target single-room or single-family house systems, this study explores the complexities within a three-story building housing six apartments. The building’s heating system consists of a hot water buffer tank, mixing loop, radiant floor heating system, and a Ground Source Heat Pump (GSHP) controlled by a weather-compensated control strategy (WCS). Our approach aims to tackle challenges like integrating real sensor data, scalability, varying weather effects, and diverse resident heat use preferences. We employ the CTSMR software to identify thermal behaviour and use reinforcement learning to design an intelligent/model-driven UPPAAL STRATEGO controller. Our results reveal a 43% reduction in energy costs while maintaining comfort levels compared to a WCS. The temporal validity of the estimated thermal models is also analyzed.

opencc-by-4.0Nov 2021View details →

Typical & Extreme Meteorological Year and Heatwaves for Dynamic Building Simulations in Belgium based on MAR model Simulations

This dataset contains 3 types of files for 12 cities (v.1.0) in Belgium: <ul> <li>Typical Meteorological Year</li> <li>Extreme Meteorological Year</li> <li>Heatwave Events</li> </ul> These files were produced from simulations of the MAR model forced by the ERA5 reanalyses over the period 1980-2020 but also by 3 ESMs from the CMIP6 database, namely BCC-CSM2-MR, MPI-ESM.1.2, and MIROC6 over the period 1980-2100. The available data for version 1.0 concerns 12 cities in Belgium but may be increased in future versions of this dataset. A detailed description of the methods used has been submitted as an article in the ESSD journal (S. Doutreloup et al., Historical and Future Weather Data for Dynamic Building Simulations in Belgium using the MAR model: Typical & Extreme Meteorological Year and Heatwaves, Earth Syst. Sci. Data, 2021).